Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations19001
Missing cells7531
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
last_review has 3758 (19.8%) missing valuesMissing
reviews_per_month has 3758 (19.8%) missing valuesMissing
minimum_nights is highly skewed (γ1 = 26.36588078)Skewed
id has unique valuesUnique
number_of_reviews has 3758 (19.8%) zerosZeros
availability_365 has 6970 (36.7%) zerosZeros

Reproduction

Analysis started2025-09-27 05:09:37.461681
Analysis finished2025-09-27 05:09:44.309875
Duration6.85 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct19001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18830405
Minimum2539
Maximum36485609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:44.356629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1177971
Q19355498
median19387536
Q328919516
95-th percentile35240782
Maximum36485609
Range36483070
Interquartile range (IQR)19564018

Descriptive statistics

Standard deviation10969858
Coefficient of variation (CV)0.5825609
Kurtosis-1.2256633
Mean18830405
Median Absolute Deviation (MAD)9804926
Skewness-0.066873715
Sum3.5779653 × 1011
Variance1.2033778 × 1014
MonotonicityNot monotonic
2025-09-27T14:09:44.431948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91386641
 
< 0.1%
179221811
 
< 0.1%
137469621
 
< 0.1%
209177121
 
< 0.1%
267342881
 
< 0.1%
105275461
 
< 0.1%
95181
 
< 0.1%
121886511
 
< 0.1%
289596081
 
< 0.1%
151581941
 
< 0.1%
Other values (18991)18991
99.9%
ValueCountFrequency (%)
25391
< 0.1%
38311
< 0.1%
50221
< 0.1%
51211
< 0.1%
52031
< 0.1%
52381
< 0.1%
58031
< 0.1%
60901
< 0.1%
68481
< 0.1%
77501
< 0.1%
ValueCountFrequency (%)
364856091
< 0.1%
364850571
< 0.1%
364802921
< 0.1%
364797231
< 0.1%
364783431
< 0.1%
364721711
< 0.1%
364718961
< 0.1%
364688801
< 0.1%
364586681
< 0.1%
364568291
< 0.1%

name
Text

Distinct18780
Distinct (%)98.9%
Missing7
Missing (%)< 0.1%
Memory size296.9 KiB
2025-09-27T14:09:44.660937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length179
Median length66
Mean length36.751395
Min length1

Characters and Unicode

Total characters698056
Distinct characters504
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18619 ?
Unique (%)98.0%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowTIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC
3rd rowVoted #1 Location Quintessential 1BR W Village Apt
4th rowSpacious 1 bedroom apartment 15min from Manhattan
5th rowBig beautiful bedroom in huge Bushwick apartment
ValueCountFrequency (%)
in6594
 
5.7%
room4014
 
3.5%
3161
 
2.7%
bedroom3016
 
2.6%
private2874
 
2.5%
apartment2658
 
2.3%
cozy2017
 
1.7%
apt1765
 
1.5%
brooklyn1623
 
1.4%
studio1556
 
1.3%
Other values (6673)86285
74.7%
2025-09-27T14:09:44.967243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97219
 
13.9%
e48065
 
6.9%
o47895
 
6.9%
t40878
 
5.9%
a40431
 
5.8%
r38293
 
5.5%
i36980
 
5.3%
n36723
 
5.3%
l20035
 
2.9%
m19434
 
2.8%
Other values (494)272103
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)698056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
97219
 
13.9%
e48065
 
6.9%
o47895
 
6.9%
t40878
 
5.9%
a40431
 
5.8%
r38293
 
5.5%
i36980
 
5.3%
n36723
 
5.3%
l20035
 
2.9%
m19434
 
2.8%
Other values (494)272103
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)698056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
97219
 
13.9%
e48065
 
6.9%
o47895
 
6.9%
t40878
 
5.9%
a40431
 
5.8%
r38293
 
5.5%
i36980
 
5.3%
n36723
 
5.3%
l20035
 
2.9%
m19434
 
2.8%
Other values (494)272103
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)698056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
97219
 
13.9%
e48065
 
6.9%
o47895
 
6.9%
t40878
 
5.9%
a40431
 
5.8%
r38293
 
5.5%
i36980
 
5.3%
n36723
 
5.3%
l20035
 
2.9%
m19434
 
2.8%
Other values (494)272103
39.0%

host_id
Real number (ℝ)

High correlation 

Distinct16241
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66394589
Minimum2571
Maximum2.7427328 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:45.041223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile764688
Q17728754
median30487854
Q31.0483536 × 108
95-th percentile2.3951918 × 108
Maximum2.7427328 × 108
Range2.7427071 × 108
Interquartile range (IQR)97106602

Descriptive statistics

Standard deviation77826632
Coefficient of variation (CV)1.1721834
Kurtosis0.28986658
Mean66394589
Median Absolute Deviation (MAD)27185317
Skewness1.2459588
Sum1.2615636 × 1012
Variance6.0569847 × 1015
MonotonicityNot monotonic
2025-09-27T14:09:45.104122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861117
 
0.6%
10743442371
 
0.4%
3028359444
 
0.2%
13735886636
 
0.2%
1224305136
 
0.2%
6139196334
 
0.2%
1609895833
 
0.2%
2254157332
 
0.2%
2637726324
 
0.1%
285674822
 
0.1%
Other values (16231)18552
97.6%
ValueCountFrequency (%)
25711
 
< 0.1%
27873
< 0.1%
31511
 
< 0.1%
34151
 
< 0.1%
35631
 
< 0.1%
36472
< 0.1%
43961
 
< 0.1%
48691
 
< 0.1%
50891
 
< 0.1%
60411
 
< 0.1%
ValueCountFrequency (%)
2742732841
< 0.1%
2741954581
< 0.1%
2741033831
< 0.1%
2738701231
< 0.1%
2738416671
< 0.1%
2737415771
< 0.1%
2736568901
< 0.1%
2736322921
< 0.1%
2736193041
< 0.1%
2736131061
< 0.1%
Distinct6307
Distinct (%)33.2%
Missing8
Missing (%)< 0.1%
Memory size296.9 KiB
2025-09-27T14:09:45.293655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.1009319
Min length1

Characters and Unicode

Total characters115875
Distinct characters138
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4077 ?
Unique (%)21.5%

Sample

1st rowIris
2nd rowJohlex
3rd rowJohn
4th rowRegan
5th rowMegan
ValueCountFrequency (%)
401
 
1.9%
and234
 
1.1%
michael175
 
0.8%
david169
 
0.8%
sonder153
 
0.7%
john145
 
0.7%
nyc122
 
0.6%
alex121
 
0.6%
laura117
 
0.6%
maria105
 
0.5%
Other values (5877)19369
91.7%
2025-09-27T14:09:45.553634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a14760
 
12.7%
e11187
 
9.7%
i9409
 
8.1%
n9394
 
8.1%
r6989
 
6.0%
l5914
 
5.1%
o4954
 
4.3%
t3638
 
3.1%
s3533
 
3.0%
h3518
 
3.0%
Other values (128)42579
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)115875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a14760
 
12.7%
e11187
 
9.7%
i9409
 
8.1%
n9394
 
8.1%
r6989
 
6.0%
l5914
 
5.1%
o4954
 
4.3%
t3638
 
3.1%
s3533
 
3.0%
h3518
 
3.0%
Other values (128)42579
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)115875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a14760
 
12.7%
e11187
 
9.7%
i9409
 
8.1%
n9394
 
8.1%
r6989
 
6.0%
l5914
 
5.1%
o4954
 
4.3%
t3638
 
3.1%
s3533
 
3.0%
h3518
 
3.0%
Other values (128)42579
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)115875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a14760
 
12.7%
e11187
 
9.7%
i9409
 
8.1%
n9394
 
8.1%
r6989
 
6.0%
l5914
 
5.1%
o4954
 
4.3%
t3638
 
3.1%
s3533
 
3.0%
h3518
 
3.0%
Other values (128)42579
36.7%

neighbourhood_group
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.9 KiB
Brooklyn
8046 
Manhattan
8031 
Queens
2331 
Bronx
 
434
Staten Island
 
159

Length

Max length13
Median length9
Mean length8.1506237
Min length5

Characters and Unicode

Total characters154870
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowManhattan
4th rowQueens
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Brooklyn8046
42.3%
Manhattan8031
42.3%
Queens2331
 
12.3%
Bronx434
 
2.3%
Staten Island159
 
0.8%

Length

2025-09-27T14:09:45.617131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T14:09:45.664443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn8046
42.0%
manhattan8031
41.9%
queens2331
 
12.2%
bronx434
 
2.3%
staten159
 
0.8%
island159
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n27191
17.6%
a24411
15.8%
o16526
10.7%
t16380
10.6%
r8480
 
5.5%
B8480
 
5.5%
l8205
 
5.3%
y8046
 
5.2%
k8046
 
5.2%
M8031
 
5.2%
Other values (10)21074
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)154870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n27191
17.6%
a24411
15.8%
o16526
10.7%
t16380
10.6%
r8480
 
5.5%
B8480
 
5.5%
l8205
 
5.3%
y8046
 
5.2%
k8046
 
5.2%
M8031
 
5.2%
Other values (10)21074
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)154870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n27191
17.6%
a24411
15.8%
o16526
10.7%
t16380
10.6%
r8480
 
5.5%
B8480
 
5.5%
l8205
 
5.3%
y8046
 
5.2%
k8046
 
5.2%
M8031
 
5.2%
Other values (10)21074
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)154870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n27191
17.6%
a24411
15.8%
o16526
10.7%
t16380
10.6%
r8480
 
5.5%
B8480
 
5.5%
l8205
 
5.3%
y8046
 
5.2%
k8046
 
5.2%
M8031
 
5.2%
Other values (10)21074
13.6%
Distinct215
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:45.971062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.919478
Min length4

Characters and Unicode

Total characters226482
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.1%

Sample

1st rowSunnyside
2nd rowHell's Kitchen
3rd rowWest Village
4th rowAstoria
5th rowBushwick
ValueCountFrequency (%)
east2575
 
8.4%
side1756
 
5.7%
harlem1540
 
5.0%
williamsburg1526
 
5.0%
bedford-stuyvesant1478
 
4.8%
heights1427
 
4.7%
upper1401
 
4.6%
village1186
 
3.9%
west1033
 
3.4%
bushwick978
 
3.2%
Other values (227)15760
51.4%
2025-09-27T14:09:46.291548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e20714
 
9.1%
i16226
 
7.2%
s15652
 
6.9%
t15047
 
6.6%
a14816
 
6.5%
l13284
 
5.9%
r13260
 
5.9%
11659
 
5.1%
n10202
 
4.5%
o9383
 
4.1%
Other values (44)86239
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)226482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e20714
 
9.1%
i16226
 
7.2%
s15652
 
6.9%
t15047
 
6.6%
a14816
 
6.5%
l13284
 
5.9%
r13260
 
5.9%
11659
 
5.1%
n10202
 
4.5%
o9383
 
4.1%
Other values (44)86239
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)226482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e20714
 
9.1%
i16226
 
7.2%
s15652
 
6.9%
t15047
 
6.6%
a14816
 
6.5%
l13284
 
5.9%
r13260
 
5.9%
11659
 
5.1%
n10202
 
4.5%
o9383
 
4.1%
Other values (44)86239
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)226482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e20714
 
9.1%
i16226
 
7.2%
s15652
 
6.9%
t15047
 
6.6%
a14816
 
6.5%
l13284
 
5.9%
r13260
 
5.9%
11659
 
5.1%
n10202
 
4.5%
o9383
 
4.1%
Other values (44)86239
38.1%

latitude
Real number (ℝ)

High correlation 

Distinct12087
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728063
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:46.358328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.64456
Q140.68882
median40.72171
Q340.76321
95-th percentile40.82645
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.07439

Descriptive statistics

Standard deviation0.05538931
Coefficient of variation (CV)0.001359979
Kurtosis0.05864488
Mean40.728063
Median Absolute Deviation (MAD)0.03659
Skewness0.2542518
Sum773873.93
Variance0.0030679757
MonotonicityNot monotonic
2025-09-27T14:09:46.428011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.718138
 
< 0.1%
40.722328
 
< 0.1%
40.686348
 
< 0.1%
40.694148
 
< 0.1%
40.686837
 
< 0.1%
40.726077
 
< 0.1%
40.680847
 
< 0.1%
40.724346
 
< 0.1%
40.763896
 
< 0.1%
40.707416
 
< 0.1%
Other values (12077)18930
99.6%
ValueCountFrequency (%)
40.508731
< 0.1%
40.522931
< 0.1%
40.538711
< 0.1%
40.539391
< 0.1%
40.541061
< 0.1%
40.543121
< 0.1%
40.54551
< 0.1%
40.548571
< 0.1%
40.548891
< 0.1%
40.549011
< 0.1%
ValueCountFrequency (%)
40.913061
< 0.1%
40.905271
< 0.1%
40.903911
< 0.1%
40.903561
< 0.1%
40.903291
< 0.1%
40.902811
< 0.1%
40.90261
< 0.1%
40.899811
< 0.1%
40.898111
< 0.1%
40.897561
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct9944
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.950827
Minimum-74.23914
Maximum-73.71795
Zeros0
Zeros (%)0.0%
Negative19001
Negative (%)100.0%
Memory size296.9 KiB
2025-09-27T14:09:46.503037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.00369
Q1-73.98205
median-73.95463
Q3-73.93449
95-th percentile-73.86186
Maximum-73.71795
Range0.52119
Interquartile range (IQR)0.04756

Descriptive statistics

Standard deviation0.046824987
Coefficient of variation (CV)-0.00063319085
Kurtosis4.8242797
Mean-73.950827
Median Absolute Deviation (MAD)0.02489
Skewness1.2340208
Sum-1405139.7
Variance0.0021925794
MonotonicityNot monotonic
2025-09-27T14:09:46.570316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.948299
 
< 0.1%
-73.957259
 
< 0.1%
-73.951219
 
< 0.1%
-73.957429
 
< 0.1%
-73.954279
 
< 0.1%
-73.985899
 
< 0.1%
-73.956758
 
< 0.1%
-73.95678
 
< 0.1%
-73.951498
 
< 0.1%
-73.980438
 
< 0.1%
Other values (9934)18915
99.5%
ValueCountFrequency (%)
-74.239141
< 0.1%
-74.212381
< 0.1%
-74.196261
< 0.1%
-74.182591
< 0.1%
-74.176281
< 0.1%
-74.173881
< 0.1%
-74.171171
< 0.1%
-74.170651
< 0.1%
-74.169661
< 0.1%
-74.166341
< 0.1%
ValueCountFrequency (%)
-73.717951
< 0.1%
-73.718291
< 0.1%
-73.725821
< 0.1%
-73.727161
< 0.1%
-73.727311
< 0.1%
-73.72741
< 0.1%
-73.727781
< 0.1%
-73.728171
< 0.1%
-73.729011
< 0.1%
-73.729281
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.9 KiB
Entire home/apt
9522 
Private room
9041 
Shared room
 
438

Length

Max length15
Median length15
Mean length13.480343
Min length11

Characters and Unicode

Total characters256140
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt9522
50.1%
Private room9041
47.6%
Shared room438
 
2.3%

Length

2025-09-27T14:09:46.632098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T14:09:46.667770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entire9522
25.1%
home/apt9522
25.1%
room9479
24.9%
private9041
23.8%
shared438
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e28523
11.1%
o28480
11.1%
r28480
11.1%
t28085
11.0%
a19001
 
7.4%
19001
 
7.4%
m19001
 
7.4%
i18563
 
7.2%
h9960
 
3.9%
p9522
 
3.7%
Other values (7)47524
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)256140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e28523
11.1%
o28480
11.1%
r28480
11.1%
t28085
11.0%
a19001
 
7.4%
19001
 
7.4%
m19001
 
7.4%
i18563
 
7.2%
h9960
 
3.9%
p9522
 
3.7%
Other values (7)47524
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)256140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e28523
11.1%
o28480
11.1%
r28480
11.1%
t28085
11.0%
a19001
 
7.4%
19001
 
7.4%
m19001
 
7.4%
i18563
 
7.2%
h9960
 
3.9%
p9522
 
3.7%
Other values (7)47524
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)256140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e28523
11.1%
o28480
11.1%
r28480
11.1%
t28085
11.0%
a19001
 
7.4%
19001
 
7.4%
m19001
 
7.4%
i18563
 
7.2%
h9960
 
3.9%
p9522
 
3.7%
Other values (7)47524
18.6%

price
Real number (ℝ)

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.34046
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:46.717317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q166
median100
Q3160
95-th percentile270
Maximum350
Range340
Interquartile range (IQR)94

Descriptive statistics

Standard deviation71.530346
Coefficient of variation (CV)0.58468268
Kurtosis0.50280069
Mean122.34046
Median Absolute Deviation (MAD)45
Skewness1.0270244
Sum2324591
Variance5116.5903
MonotonicityNot monotonic
2025-09-27T14:09:46.790193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100856
 
4.5%
150821
 
4.3%
50636
 
3.3%
200590
 
3.1%
75570
 
3.0%
60555
 
2.9%
80531
 
2.8%
70482
 
2.5%
120471
 
2.5%
65471
 
2.5%
Other values (311)13018
68.5%
ValueCountFrequency (%)
106
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
151
 
< 0.1%
163
 
< 0.1%
181
 
< 0.1%
193
 
< 0.1%
2013
0.1%
213
 
< 0.1%
ValueCountFrequency (%)
350147
0.8%
34914
 
0.1%
3481
 
< 0.1%
3472
 
< 0.1%
3461
 
< 0.1%
34511
 
0.1%
3441
 
< 0.1%
3432
 
< 0.1%
3421
 
< 0.1%
3412
 
< 0.1%

minimum_nights
Real number (ℝ)

Skewed 

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9068996
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:46.855768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.456544
Coefficient of variation (CV)3.1065377
Kurtosis1156.4552
Mean6.9068996
Median Absolute Deviation (MAD)1
Skewness26.365881
Sum131238
Variance460.38328
MonotonicityNot monotonic
2025-09-27T14:09:46.923103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15003
26.3%
24619
24.3%
33086
16.2%
301442
 
7.6%
41253
 
6.6%
51140
 
6.0%
7822
 
4.3%
6285
 
1.5%
14210
 
1.1%
10178
 
0.9%
Other values (65)963
 
5.1%
ValueCountFrequency (%)
15003
26.3%
24619
24.3%
33086
16.2%
41253
 
6.6%
51140
 
6.0%
6285
 
1.5%
7822
 
4.3%
852
 
0.3%
934
 
0.2%
10178
 
0.9%
ValueCountFrequency (%)
12501
 
< 0.1%
9992
 
< 0.1%
4801
 
< 0.1%
4001
 
< 0.1%
3701
 
< 0.1%
36511
0.1%
3641
 
< 0.1%
3001
 
< 0.1%
2991
 
< 0.1%
2402
 
< 0.1%

number_of_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.797747
Minimum0
Maximum607
Zeros3758
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:46.986932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q324
95-th percentile116
Maximum607
Range607
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.493455
Coefficient of variation (CV)1.9116706
Kurtosis19.565921
Mean23.797747
Median Absolute Deviation (MAD)6
Skewness3.7060188
Sum452181
Variance2069.6544
MonotonicityNot monotonic
2025-09-27T14:09:47.056540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03758
19.8%
12022
 
10.6%
21342
 
7.1%
3975
 
5.1%
4796
 
4.2%
5589
 
3.1%
6544
 
2.9%
7495
 
2.6%
8449
 
2.4%
9383
 
2.0%
Other values (311)7648
40.3%
ValueCountFrequency (%)
03758
19.8%
12022
10.6%
21342
 
7.1%
3975
 
5.1%
4796
 
4.2%
5589
 
3.1%
6544
 
2.9%
7495
 
2.6%
8449
 
2.4%
9383
 
2.0%
ValueCountFrequency (%)
6071
< 0.1%
5941
< 0.1%
5101
< 0.1%
4881
< 0.1%
4741
< 0.1%
4671
< 0.1%
4661
< 0.1%
4591
< 0.1%
4481
< 0.1%
4411
< 0.1%

last_review
Date

Missing 

Distinct1494
Distinct (%)9.8%
Missing3758
Missing (%)19.8%
Memory size296.9 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-27T14:09:47.121414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:47.190690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation  Missing 

Distinct789
Distinct (%)5.2%
Missing3758
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean1.3809276
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:47.254344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.01
95-th percentile4.69
Maximum27.95
Range27.94
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.6899884
Coefficient of variation (CV)1.2238066
Kurtosis11.9516
Mean1.3809276
Median Absolute Deviation (MAD)0.62
Skewness2.4353306
Sum21049.48
Variance2.8560608
MonotonicityNot monotonic
2025-09-27T14:09:47.317019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02362
 
1.9%
1352
 
1.9%
0.05333
 
1.8%
0.03323
 
1.7%
0.04268
 
1.4%
0.08256
 
1.3%
0.16244
 
1.3%
0.09235
 
1.2%
0.06226
 
1.2%
0.11217
 
1.1%
Other values (779)12427
65.4%
(Missing)3758
 
19.8%
ValueCountFrequency (%)
0.0117
 
0.1%
0.02362
1.9%
0.03323
1.7%
0.04268
1.4%
0.05333
1.8%
0.06226
1.2%
0.07168
0.9%
0.08256
1.3%
0.09235
1.2%
0.1191
1.0%
ValueCountFrequency (%)
27.951
< 0.1%
20.941
< 0.1%
19.751
< 0.1%
17.821
< 0.1%
16.221
< 0.1%
13.451
< 0.1%
13.421
< 0.1%
13.241
< 0.1%
13.151
< 0.1%
12.991
< 0.1%
Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5838114
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:47.385271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile13
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation31.15475
Coefficient of variation (CV)4.7320235
Kurtosis77.214814
Mean6.5838114
Median Absolute Deviation (MAD)0
Skewness8.4612229
Sum125099
Variance970.61846
MonotonicityNot monotonic
2025-09-27T14:09:47.449713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
112627
66.5%
22605
 
13.7%
31123
 
5.9%
4539
 
2.8%
5345
 
1.8%
6208
 
1.1%
7165
 
0.9%
8164
 
0.9%
327117
 
0.6%
998
 
0.5%
Other values (37)1010
 
5.3%
ValueCountFrequency (%)
112627
66.5%
22605
 
13.7%
31123
 
5.9%
4539
 
2.8%
5345
 
1.8%
6208
 
1.1%
7165
 
0.9%
8164
 
0.9%
998
 
0.5%
1069
 
0.4%
ValueCountFrequency (%)
327117
0.6%
23271
0.4%
12144
 
0.2%
10336
 
0.2%
9669
0.4%
9134
 
0.2%
8732
 
0.2%
6519
 
0.1%
5241
 
0.2%
5016
 
0.1%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.72538
Minimum0
Maximum365
Zeros6970
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2025-09-27T14:09:47.513720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q3219
95-th percentile358
Maximum365
Range365
Interquartile range (IQR)219

Descriptive statistics

Standard deviation130.5999
Coefficient of variation (CV)1.1902433
Kurtosis-0.93482914
Mean109.72538
Median Absolute Deviation (MAD)39
Skewness0.80270185
Sum2084892
Variance17056.334
MonotonicityNot monotonic
2025-09-27T14:09:47.579059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06970
36.7%
365432
 
2.3%
364195
 
1.0%
1176
 
0.9%
5133
 
0.7%
3115
 
0.6%
2113
 
0.6%
179112
 
0.6%
89111
 
0.6%
6110
 
0.6%
Other values (356)10534
55.4%
ValueCountFrequency (%)
06970
36.7%
1176
 
0.9%
2113
 
0.6%
3115
 
0.6%
4107
 
0.6%
5133
 
0.7%
6110
 
0.6%
796
 
0.5%
885
 
0.4%
983
 
0.4%
ValueCountFrequency (%)
365432
2.3%
364195
1.0%
36388
 
0.5%
36262
 
0.3%
36144
 
0.2%
36033
 
0.2%
35949
 
0.3%
35853
 
0.3%
35733
 
0.2%
35626
 
0.1%

Interactions

2025-09-27T14:09:43.468121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.164482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.736235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.494907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.077841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.639263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.260979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.812207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.366698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.903194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.526285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.224104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.789022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.558070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.134169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.697683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.322938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.867270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.421765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.958483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.580746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.277242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.838365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.612576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.185447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.800627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.375816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.919241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.471845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.020618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.639732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.340441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.894679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.671130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.249802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.873633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.433095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.983323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.528284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.079295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.699581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.395604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.947103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.727078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.301808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.928148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.486270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.035881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.578228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.134613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.754535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.453322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.999741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.785802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.362512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.987664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.540783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.090803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.630654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.189768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.810443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.508250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.283039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.843394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.418020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.042226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.593365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.145846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.691778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.244467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.866773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.563872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.336019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.905616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.473154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.097821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.652875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.199015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.745196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.298796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.920171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.617523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.386285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.960299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.521998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.150461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.703424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.251152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.795941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.358674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.977344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:38.679554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:39.439664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.018069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:40.585238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.205016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:41.757882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.311902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:42.850340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-27T14:09:43.413365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-27T14:09:47.637939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.4110.1640.157-0.0200.0800.0780.0790.2570.0590.3970.093
calculated_host_listings_count0.4111.0000.1420.136-0.0020.0680.0630.0850.066-0.1180.1530.094
host_id0.1640.1421.0000.5520.0350.123-0.1470.102-0.114-0.1020.2710.099
id0.1570.1360.5521.000-0.0080.080-0.0730.060-0.302-0.0460.3650.073
latitude-0.020-0.0020.035-0.0081.0000.0480.0160.537-0.0320.132-0.0300.104
longitude0.0800.0680.1230.0800.0481.000-0.1230.6470.072-0.4180.1270.142
minimum_nights0.0780.063-0.147-0.0730.016-0.1231.0000.000-0.1640.106-0.2920.021
neighbourhood_group0.0790.0850.1020.0600.5370.6470.0001.0000.0260.1810.0670.115
number_of_reviews0.2570.066-0.114-0.302-0.0320.072-0.1640.0261.000-0.0200.7070.000
price0.059-0.118-0.102-0.0460.132-0.4180.1060.181-0.0201.000-0.0200.497
reviews_per_month0.3970.1530.2710.365-0.0300.127-0.2920.0670.707-0.0201.0000.016
room_type0.0930.0940.0990.0730.1040.1420.0210.1150.0000.4970.0161.000

Missing values

2025-09-27T14:09:44.074170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-27T14:09:44.164801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-27T14:09:44.264203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
131444015TIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC8523790JohlexManhattanHell's Kitchen40.76682-73.98878Entire home/apt17030NaTNaN1188
28741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
334602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
423203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
54402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
630070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
734231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
85856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
97929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
199905192459Quiet Room in 4BR UWS Brownstone10677483GregManhattanUpper West Side40.80173-73.96625Private room7010NaTNaN10
199911327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1999223612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1999334485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1999425616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
199957094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
199964424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
199974545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1999826518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1999933631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297